Development and Application of Mass Spectrometry-based Single-cell Proteomics Technologies

doi:10.13560/j.cnki.biotech.bull.1985.2024-0360

Abstract

Abstract:

Single-cell multi-omics technologies have brought new vitality into life sciences and medicine and enable us to explore the unknowns of life and expand the boundaries of medicine from a more microscopic cellular scale, a higher-dimensional spatial-temporal perspective, and a more intersectional vision of genomics. Single-cell proteomics（SCP）is the study of the temporal and spatial heterogeneity of protein expression in a single or a single type of cell. Despite technical difficulties such as throughput, sensitivity, and resolution, mass spectrometry-based cell-scale spatiotemporal proteomics technologies have attracted much attention due to its characteristics of unbiased detection, multiple identification types and accurate quantification. This paper reviews the development of single-cell proteomics in recent years, introduces the related technological advances in four aspects, namely, single-cell sorting, sample pre-treatment, mass spectrometry detection, and data analysis, and discusses its applications and prospects for development in life science and medical research. Spatiotemporal proteomics research at the single-cell level presents both challenges and opportunities, and the emerging single-cell proteomics technologies will undoubtedly provide new ideas and methods, cognitive clues, and multimodal data, and other valuable resources for related research fields.

Key words: single-cell proteomics（SCP）, mass spectrometry（MS）, cell-scale spatiotemporal proteomics technology

GU Lei, ZHANG Yu-lu, TANG Shang-rui, YU Hao-yue, LI Chen. Development and Application of Mass Spectrometry-based Single-cell Proteomics Technologies[J]. Biotechnology Bulletin, 2024, 40(11): 125-141.

Figures/Tables 5

References 72

[1]	Aslam B, Basit M, Nisar MA, et al. Proteomics: technologies and their applications[J]. J Chromatogr Sci, 2017, 55(2): 182-196.
[2]	Kelly RT. Single-cell proteomics: progress and prospects[J]. Mol Cell Proteomics, 2020, 19(11): 1739-1748.
[3]	Vistain LF, Tay S. Single-cell proteomics[J]. Trends Biochem Sci, 2021, 46(8): 661-672.
[4]	Woo J, Williams SM, Markillie LM, et al. High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip[J]. Nat Commun, 2021, 12(1): 6246.
[5]	Brunner AD, Thielert M, Vasilopoulou C, et al. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation[J]. Mol Syst Biol, 2022, 18(3): e10798.
[6]	Jiang YR, Zhu L, Cao LR, et al. Simultaneous deep transcriptome and proteome profiling in a single mouse oocyte[J]. Cell Rep, 2023, 42(11): 113455.
[7]	Levy E, Slavov N. Single cell protein analysis for systems biology[J]. Essays Biochem, 2018, 62(4): 595-605.
[8]	Lundberg E, Borner GHH. Spatial proteomics: a powerful discovery tool for cell biology[J]. Nat Rev Mol Cell Biol, 2019, 20(5): 285-302.
[9]	Cheung TK, Lee CY, Bayer FP, et al. Defining the carrier proteome limit for single-cell proteomics[J]. Nat Meth, 2021, 18: 76-83.
[10]	Shao X, Wang XT, Guan S, et al. Integrated proteome analysis device for fast single-cell protein profiling[J]. Anal Chem, 2018, 90(23): 14003-14010.
[11]	Li ZY, Huang M, Wang XK, et al. Nanoliter-scale oil-air-droplet chip-based single cell proteomic analysis[J]. Anal Chem, 2018, 90(8): 5430-5438.
[12]	Zhu Y, Piehowski PD, Zhao R, et al. Nanodroplet processing platform for deep and quantitative proteome profiling of 10-100 mammalian cells[J]. Nat Commun, 2018, 9(1): 882.
[13]	Lombard-Banek C, Moody SA, Manzini MC, et al. Microsampling capillary electrophoresis mass spectrometry enables single-cell proteomics in complex tissues: developing cell clones in live Xenopus laevis and zebrafish embryos[J]. Anal Chem, 2019, 91(7): 4797-4805.
[14]	Greguš M, Kostas JC, Ray S, et al. Improved sensitivity of ultralow flow LC-MS-based proteomic profiling of limited samples using monolithic capillary columns and FAIMS technology[J]. Anal Chem, 2020, 92(21): 14702-14712.
[15]	Tsai CF, Zhang PF, Scholten D, et al. Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics[J]. Commun Biol, 2021, 4(1): 265.
[16]	Gebreyesus ST, Siyal AA, Kitata RB, et al. Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry[J]. Nat Commun, 2022, 13(1): 37.
[17]	Wang Y, Guan ZY, Shi SW, et al. Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell[J]. Nat Commun, 2024, 15: 1279.
[18]	Tsai CF, Wang YT, Hsu CC, et al. A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics[J]. Commun Biol, 2023, 6: 70.
[19]	Budnik B, Levy E, Harmange G, et al. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation[J]. Genome Biol, 2018, 19(1): 161.
[20]	Specht H, Emmott E, Petelski AA, et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2[J]. Genome Biol, 2021, 22(1): 50.
[21]	Liang YR, Acor H, McCown MA, et al. Fully automated sample processing and analysis workflow for low-input proteome profiling[J]. Anal Chem, 2021, 93(3): 1658-1666.
[22]	Li SQ, Su KC, Zhuang ZK, et al. A simple, rapid, and practical method for single-cell proteomics based on mass-adaptive coating of synthetic peptides[J]. Sci Bull, 2022, 67(6): 581-584.
[23]	Masuda T, Inamori Y, Furukawa A, et al. Water droplet-in-oil digestion method for single-cell proteomics[J]. Anal Chem, 2022, 94(29): 10329-10336.
[24]	Leduc A, Huffman RG, Cantlon J, et al. Exploring functional protein covariation across single cells using nPOP[J]. Genome Biol, 2022, 23(1): 261.
[25]	Gu L, Li Z, Wang Q, et al. An ultra-sensitive and easy-to-use multiplexed single-cell proteomic analysis[J]. bioRxiv, 2022. DOI: https://doi.org/10.1101/2022.01.02.474723.
[26]	Gu L, Li XM, Zhu WC, et al. Ultrasensitive proteomics depicted an in-depth landscape for the very early stage of mouse maternal-to-zygotic transition[J]. J Pharm Anal, 2023, 13(8): 942-954.
[27]	Matzinger M, Müller E, Dürnberger G, et al. Robust and easy-to-use one-pot workflow for label-free single-cell proteomics[J]. Anal Chem, 2023, 95(9): 4435-4445.
[28]	Ctortecka C, Hartlmayr D, Seth A, et al. An automated nanowell-array workflow for quantitative multiplexed single-cell proteomics sample preparation at high sensitivity[J]. Mol Cell Proteomics, 2023, 22(12): 100665.
[29]	Gu L, Li X, Li Z, et al. Increasing the sensitivity, recovery, and integrality of spatially resolved proteomics by LCM-MTA[J]. bioRxiv, 2022. DOI: https://doi.org/10.1101/2022.08.21.504675.
[30]	Mund A, Coscia F, Kriston A, et al. Deep Visual Proteomics defines single-cell identity and heterogeneity[J]. Nat Biotechnol, 2022, 40(8): 1231-1240.
[31]	Rosenberger FA, Thielert M, Strauss MT, et al. Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome[J]. Nat Methods, 2023, 20(10): 1530-1536.
[32]	Li L, Sun CJ, Sun YT, et al. Spatially resolved proteomics via tissue expansion[J]. Nat Commun, 2022, 13(1): 7242.
[33]	Tsai CF, Zhao R, Williams SM, et al. An improved boosting to amplify signal with isobaric labeling(iBASIL)strategy for precise quantitative single-cell proteomics[J]. Mol Cell Proteomics, 2020, 19(5): 828-838.
[34]	Bhatia HS, Brunner AD, Öztürk F, et al. Spatial proteomics in three-dimensional intact specimens[J]. Cell, 2022, 185(26): 5040-5058.e19.
[35]	Xu Y, Wang X, Li Y, et al. Multimodal single cell-resolved spatial proteomics reveals pancreatic tumor heterogeneity[J]. bioRxiv, 2023. DOI: https://doi.org/10.1101/2023.11.04.565590.
[36]	Dang YJ, Zhu L, Yuan P, et al. Functional profiling of stage-specific proteome and translational transition across human pre-implantation embryo development at a single-cell resolution[J]. Cell Discov, 2023, 9(1): 10.
[37]	He YY, Yuan HM, Liang Y, et al. On-capillary alkylation micro-reactor: a facile strategy for proteo-metabolome profiling in the same single cells[J]. Chem Sci, 2023, 14(46): 13495-13502.
[38]	Zhu WC, Ding YF, Meng J, et al. Reading and writing of mRNA m⁶A modification orchestrate maternal-to-zygotic transition in mice[J]. Genome Biol, 2023, 24(1): 67.
[39]	Gross A, Schoendube J, Zimmermann S, et al. Technologies for single-cell isolation[J]. Int J Mol Sci, 2015, 16(8): 16897-16919.
[40]	Yokoyama WM, Christensen M, Dos Santos G, et al. Production of monoclonal antibodies[J]. Curr Protoc Immunol, 2013, 102(1): 2.5.1-2.5.29.
[41]	Wright G, Tucker MJ, Morton PC, et al. Micromanipulation in assisted reproduction: a review of current technology[J]. Curr Opin Obstet Gynecol, 1998, 10(3): 221-226.
[42]	van den Brink SC, Sage F, Vértesy Á, et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations[J]. Nat Methods, 2017, 14(10): 935-936.
[43]	Nakamura N, Ruebel K, Jin L, et al. Laser capture microdissection for analysis of single cells[J]. Methods Mol Med, 2007, 132: 11-18.
[44]	宋扬, 林金明. 微流控芯片上单细胞操控与分析方法研究进展[J]. 中国科学: 化学, 2023, 53(8): 1472-1493.
	Song Y, Lin JM. Recent advances in single-cell manipulation and analysis methods on microfluidic chips[J]. Sci Sin Chim, 2023, 53(8): 1472-1493.
[45]	Hosic S, Murthy SK, Koppes AN. Microfluidic sample preparation for single cell analysis[J]. Anal Chem, 2016, 88(1): 354-380.
[46]	Liu D, Sun ML, Zhang JW, et al. Single-cell droplet microfluidics for biomedical applications[J]. Analyst, 2022, 147(11): 2294-2316.
[47]	Gao Y, Wu MR, Lin Y, et al. Acoustic microfluidic separation techniques and bioapplications: a review[J]. Micromachines, 2020, 11(10): 921.
[48]	贺映云, 袁辉明, 梁振, 等. 单细胞蛋白质组学分析技术研究进展[J]. 分析试验室, 2022, 41(12): 1365-1378.
	He YY, Yuan HM, Liang Z, et al. Progress in technologies for single-cell proteome analysis[J]. Chin J Anal Lab, 2022, 41(12): 1365-1378.
[49]	Shen Y, Tolicć N, Masselon C, et al. Nanoscale proteomics[J]. Anal Bioanal Chem, 2004, 378(4): 1037-1045.
[50]	Shen YF, Zhao R, Berger SJ, et al. High-efficiency nanoscale liquid chromatography coupled on-line with mass spectrometry using nanoelectrospray ionization for proteomics[J]. Anal Chem, 2002, 74(16): 4235-4249.
[51]	Shen YF, Tolić N, Masselon C, et al. Ultrasensitive proteomics using high-efficiency on-line micro-SPE-nanoLC-nanoESI MS and MS/MS[J]. Anal Chem, 2004, 76(1): 144-154.
[52]	Zhu Y, Clair G, Chrisler WB, et al. Proteomic analysis of single mammalian cells enabled by microfluidic nanodroplet sample preparation and ultrasensitive NanoLC-MS[J]. Angew Chem Int Ed, 2018, 57(38): 12370-12374.
[53]	Cong YZ, Liang YR, Motamedchaboki K, et al. Improved single-cell proteome coverage using narrow-bore packed NanoLC columns and ultrasensitive mass spectrometry[J]. Anal Chem, 2020, 92(3): 2665-2671.
[54]	Orsburn BC. Acetic acid is a superior acidifier for sub-nanogram and single cell proteomic studies[J]. bioRxiv, 2023. DOI: https://doi.org/10.1101/2023.08.01.551522.
[55]	Ye ZL, Sabatier P, Van der Hoeven L, et al. High-throughput and scalable single cell proteomics identifies over 5000 proteins per cell[J]. bioRxiv, 2023. DOI: https://doi.org/10.1101/2023.11.27.568953.
[56]	Bubis JA, Arrey TN, Damoc E, et al. Challenging the Astral^TM mass analyzer - up to 5300 proteins per single-cell at unseen quantitative accuracy to study cellular heterogeneity[J]. bioRxiv, 2024. DOI: https://doi.org/10.1101/2024.02.01.578358
[57]	Ctortecka C, Clark NM, Boyle B, et al. Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications[J]. bioRxiv, 2024. DOI: https://doi.org/10.1101/2024.01.20.576369.
[58]	Stadlmann J, Hudecz O, Krššáková G, et al. Improved sensitivity in low-input proteomics using micropillar array-based chromatography[J]. Anal Chem, 2019, 91(22): 14203-14207.
[59]	Amenson-Lamar EA, Sun LL, Zhang ZB, et al. Detection of 1 zmol injection of angiotensin using capillary zone electrophoresis coupled to a Q-Exactive HF mass spectrometer with an electrokinetically pumped sheath-flow electrospray interface[J]. Talanta, 2019, 204: 70-73.
[60]	Dou MW, Zhu Y, Liyu A, et al. Nanowell-mediated two-dimensional liquid chromatography enables deep proteome profiling of <1000 mammalian cells[J]. Chem Sci, 2018, 9(34): 6944-6951.
[61]	Krieger JR, Wybenga-Groot LE, Tong JF, et al. Evosep one enables robust deep proteome coverage using tandem mass tags while significantly reducing instrument time[J]. J Proteome Res, 2019, 18(5): 2346-2353.
[62]	Zheng RS, Stejskal K, Pynn C, et al. Deep single-shot NanoLC-MS proteome profiling with a 1500 bar UHPLC system, long fully porous columns, and HRAM MS[J]. J Proteome Res, 2022, 21(10): 2545-2551.
[63]	Zhu Y, Zhao R, Piehowski PD, et al. Subnanogram proteomics: impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples[J]. Int J Mass Spectrom, 2018, 427: 4-10.
[64]	Cox J. Prediction of peptide mass spectral libraries with machine learning[J]. Nat Biotechnol, 2023, 41(1): 33-43.
[65]	Michalski A, Cox J, Mann M. More than 100, 000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS[J]. J Proteome Res, 2011, 10(4): 1785-1793.
[66]	Guan SH, Taylor PP, Han ZW, et al. Data dependent-independent acquisition(DDIA)proteomics[J]. J Proteome Res, 2020, 19(8): 3230-3237.
[67]	Schoof EM, Furtwängler B, Üresin N, et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies[J]. Nat Commun, 2021, 12(1): 3341.
[68]	Kalxdorf M, Müller T, Stegle O, et al. IceR improves proteome coverage and data completeness in global and single-cell proteomics[J]. Nat Commun, 2021, 12(1): 4787.
[69]	Wang B, Wang Y, Chen Y, et al. DeepSCP: utilizing deep learning to boost single-cell proteome coverage[J]. Brief Bioinform, 2022, 23(4): bbac214.
[70]	Huang Z, Merrihew GE, Larson EB, et al. Brain proteomic analysis implicates actin filament processes and injury response in resilience to Alzheimer’s disease[J]. Nat Commun, 2023, 14(1): 2747.
[71]	Sun FY, Li HY, Sun DQ, et al. Single-cell omics: experimental workflow, data analyses and applications[J]. Sci China Life Sci, 2024. DOI: 10.1007/s11427-023-2561-0.
[72]	Malioutov D, Chen TC, Airoldi E, et al. Quantifying homologous proteins and proteoforms[J]. Mol Cell Proteomics, 2019, 18(1): 162-168.

单细胞蛋白质组学工具 Single-cell proteomics tools	专用设备 Customized equipment	标记 Label	细胞类型 Cell types	细胞分选分离方法 Cell isolation methods	质谱仪 Mass spectrometers	样品前处理通量 Pretreatment throughput（Cells per run）	质谱检测通量 MS throughput（Cells per day）	单个细胞中平均鉴定的蛋白质数目 Average identified protein number per cell	单细胞蛋白质组覆盖总深度Depth of proteome coverage（n = number of cells）	参考文献 References	技术应用 Technical applications	应用相关文献Related references
iPAD-1	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Tribrid MS	1	24	271	406（n=10）	[10]	图谱研究	[10]
OAD	需要	非标记	HeLa	液滴微流控技术	Orbitrap Elite MS	1	4	51	/	[11]	发育研究	[36]
nanoPOTS	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Lumos Tribrid MS	27	/	669	/	[12]	疾病精准分析	[12]
CE-ESI-MS	需要	非标记	Xenopus laevis（D11）, zebrafish embryos	毛细管电泳	Orbitrap Q-Exactive Plus	/	/	450-800	/	[13]	发育研究	[13]
ULF LC-FAIMS-MS	需要	非标记	HeLa, U-937	纳升液相色谱毛细管	Orbitrap Fusion Lumos Tribrid MS	5	/	2 348	/	[14]	癌症研究	[14]
nested nanoPOTS	需要	TMT16	C10, RAW, SVEC	全自动单细胞分选技术	Orbitrap Eclipse Tribrid MS	243	108	1 716	2 457（n=108）	[4]	免疫研究	[4]
iProChip	需要	非标记	MEC-1	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	9	9	455	/	[16]	癌症研究	[16]
SciProChip	需要	非标记	PC-9	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	20	16	1 500	1 995（n=10）	[16]	癌症研究	[16]
PiSPA	需要	非标记	A549	液滴微流控技术	timsTOF Pro	1	/	3 008	5 093（n=37）	[17]	癌症研究	[17]
nPOP	需要	TMT18	U-937, WM989	全自动单细胞分选技术	Orbitrap Q-Exactive	2 016	212	997	2 844（n=1543）	[24]	癌症研究	[24]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 940（20× carrier）	3 674（n=276）	[28]	癌症研究	[28]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 598（no carrier）	3 674（n=276）	[28]	癌症研究	[28]
scSTAP	需要	非标记	小鼠oocytes	序控液滴微流控技术	timsTOF Pro	/	15-20	2 663	3 363（n=36）	[6]	发育研究	[6]
OCAM	需要	非标记	HeLa, 小鼠oocytes	毛细管烷基化微反应	Orbitrap Q-Exactive, Orbitrap Exploris 480 MS	100	/	3 457 (single mouse oocyte) 1 509 (single HeLa cell)	/	[37]	发育研究	[37]
SCoPE	不需要	TMT10	Jurkat, U-937	人工挑选	LTQ Orbitrap Elite	8	48	/	767（n=24）	[19]	图谱研究	[19]
SCoPE2	不需要	TMT16	Monocyte, macrophage	流式细胞荧光分选术	Orbitrap Q-Exactive	/	200	1 000	3 042（n=1490）	[20]	免疫研究	[20]
SOP-MS	不需要	非标记	MCF10A, MCF7	流式细胞荧光分选术，激光捕获显微切割	Orbitrap Q-Exactive Plus	/	/	146	/	[15]	癌症研究	[15]
SOP/C18-IMAC-C18/iBASIL	不需要	TMT标记	AML	流式细胞荧光分选术	Orbitrap Q-Exactive Plus	/	/	1 926	2 622（n=104）	[33]	免疫研究	[18]
autoPOTS	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Exploris 480 MS	/	10	301	/	[21]	免疫研究	[21]
T-SCP	不需要	非标记	HeLa	流式细胞荧光分选术	timsTOF Pro	308	41	2 083	2 501（n=231）	[5]	图谱研究	[5]
Mad-CASP	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Eclipse Tribrid MS	/	16	1 240	/	[22]	疾病精准分析	[22]
WinO	不需要	TMT10	RPMI8226	细胞分选仪	Orbitrap Fusion Tribrid MS	/	144	845	/	[23]	免疫研究	[23]
UE-SCP	不需要	TMT6	HeLa, HEK-293T	全自动单细胞分选技术	timsTOF Pro	308	96	2 249	4 230（n=128）	[25]	癌症研究	[25]
CS-UPT	不需要	非标记, TMT6, TMT8	小鼠MII oocytes, zygotes	人工挑选	timsTOF Pro	/	18 非标记,（70, 50×car rier,100×carrier）,（140, no carrier)	2 665非标记,（2 182, 50× carrier）,（2 371, 100× carrier）,（1 568, no carrier）	/	[26]	发育研究	[26,38]
REO-SCP	不需要	非标记	HeLa	全自动单细胞分选技术	Orbitrap Exploris 480 MS	384	/	1 208	/	[27]	图谱研究	[27]
DVP	不需要	非标记	U2OS	激光显微切割技术	timsTOF Pro	/	/	4 500-4 800（每个样品平均267个细胞核）	5 085（8个重复）	[30]	癌症研究	[30]
scDVP	不需要	非标记	小鼠肝细胞	激光显微切割技术	timsTOF SCP	/	80	1 700-2 700（1/3-1/2个细胞的体积）	/	[31]	图谱研究	[31]
LCM-MTA	不需要	非标记	人结直肠癌癌组织与癌旁组织	激光显微切割技术	timsTOF Pro	/	/	536（15个细胞的体积）	/	[29]	癌症研究	[29]
ProteomEx	不需要	非标记	小鼠脑组织	激光显微切割技术	timsTOF Pro	/	/	928（160 μm横向分辨率）	/	[32]	图谱研究	[32]
DISCO-MS	不需要	非标记	小鼠脑、心脏和肺组织	激光显微切割技术	timsTOF Pro	/	/	1 400（每个ROI）	/	[34]	癌症研究	[34]
SCPro	不需要	非标记	HEK 293T	激光显微切割技术	timsTOF Pro	/	/	800（2.4个细胞的体积）	/	[35]	疾病精准分析	[35]

单细胞蛋白质组学工具 Single-cell proteomics tools	专用设备 Customized equipment	标记 Label	细胞类型 Cell types	细胞分选分离方法 Cell isolation methods	质谱仪 Mass spectrometers	样品前处理通量 Pretreatment throughput（Cells per run）	质谱检测通量 MS throughput（Cells per day）	单个细胞中平均鉴定的蛋白质数目 Average identified protein number per cell	单细胞蛋白质组覆盖总深度Depth of proteome coverage（n = number of cells）	参考文献 References	技术应用 Technical applications	应用相关文献Related references
iPAD-1	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Tribrid MS	1	24	271	406（n=10）	[10]	图谱研究	[10]
OAD	需要	非标记	HeLa	液滴微流控技术	Orbitrap Elite MS	1	4	51	/	[11]	发育研究	[36]
nanoPOTS	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Lumos Tribrid MS	27	/	669	/	[12]	疾病精准分析	[12]
CE-ESI-MS	需要	非标记	Xenopus laevis（D11）, zebrafish embryos	毛细管电泳	Orbitrap Q-Exactive Plus	/	/	450-800	/	[13]	发育研究	[13]
ULF LC-FAIMS-MS	需要	非标记	HeLa, U-937	纳升液相色谱毛细管	Orbitrap Fusion Lumos Tribrid MS	5	/	2 348	/	[14]	癌症研究	[14]
nested nanoPOTS	需要	TMT16	C10, RAW, SVEC	全自动单细胞分选技术	Orbitrap Eclipse Tribrid MS	243	108	1 716	2 457（n=108）	[4]	免疫研究	[4]
iProChip	需要	非标记	MEC-1	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	9	9	455	/	[16]	癌症研究	[16]
SciProChip	需要	非标记	PC-9	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	20	16	1 500	1 995（n=10）	[16]	癌症研究	[16]
PiSPA	需要	非标记	A549	液滴微流控技术	timsTOF Pro	1	/	3 008	5 093（n=37）	[17]	癌症研究	[17]
nPOP	需要	TMT18	U-937, WM989	全自动单细胞分选技术	Orbitrap Q-Exactive	2 016	212	997	2 844（n=1543）	[24]	癌症研究	[24]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 940（20× carrier）	3 674（n=276）	[28]	癌症研究	[28]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 598（no carrier）	3 674（n=276）	[28]	癌症研究	[28]
scSTAP	需要	非标记	小鼠oocytes	序控液滴微流控技术	timsTOF Pro	/	15-20	2 663	3 363（n=36）	[6]	发育研究	[6]
OCAM	需要	非标记	HeLa, 小鼠oocytes	毛细管烷基化微反应	Orbitrap Q-Exactive, Orbitrap Exploris 480 MS	100	/	3 457 (single mouse oocyte) 1 509 (single HeLa cell)	/	[37]	发育研究	[37]
SCoPE	不需要	TMT10	Jurkat, U-937	人工挑选	LTQ Orbitrap Elite	8	48	/	767（n=24）	[19]	图谱研究	[19]
SCoPE2	不需要	TMT16	Monocyte, macrophage	流式细胞荧光分选术	Orbitrap Q-Exactive	/	200	1 000	3 042（n=1490）	[20]	免疫研究	[20]
SOP-MS	不需要	非标记	MCF10A, MCF7	流式细胞荧光分选术，激光捕获显微切割	Orbitrap Q-Exactive Plus	/	/	146	/	[15]	癌症研究	[15]
SOP/C18-IMAC-C18/iBASIL	不需要	TMT标记	AML	流式细胞荧光分选术	Orbitrap Q-Exactive Plus	/	/	1 926	2 622（n=104）	[33]	免疫研究	[18]
autoPOTS	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Exploris 480 MS	/	10	301	/	[21]	免疫研究	[21]
T-SCP	不需要	非标记	HeLa	流式细胞荧光分选术	timsTOF Pro	308	41	2 083	2 501（n=231）	[5]	图谱研究	[5]
Mad-CASP	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Eclipse Tribrid MS	/	16	1 240	/	[22]	疾病精准分析	[22]
WinO	不需要	TMT10	RPMI8226	细胞分选仪	Orbitrap Fusion Tribrid MS	/	144	845	/	[23]	免疫研究	[23]
UE-SCP	不需要	TMT6	HeLa, HEK-293T	全自动单细胞分选技术	timsTOF Pro	308	96	2 249	4 230（n=128）	[25]	癌症研究	[25]
CS-UPT	不需要	非标记, TMT6, TMT8	小鼠MII oocytes, zygotes	人工挑选	timsTOF Pro	/	18 非标记,（70, 50×car rier,100×carrier）,（140, no carrier)	2 665非标记,（2 182, 50× carrier）,（2 371, 100× carrier）,（1 568, no carrier）	/	[26]	发育研究	[26,38]
REO-SCP	不需要	非标记	HeLa	全自动单细胞分选技术	Orbitrap Exploris 480 MS	384	/	1 208	/	[27]	图谱研究	[27]
DVP	不需要	非标记	U2OS	激光显微切割技术	timsTOF Pro	/	/	4 500-4 800（每个样品平均267个细胞核）	5 085（8个重复）	[30]	癌症研究	[30]
scDVP	不需要	非标记	小鼠肝细胞	激光显微切割技术	timsTOF SCP	/	80	1 700-2 700（1/3-1/2个细胞的体积）	/	[31]	图谱研究	[31]
LCM-MTA	不需要	非标记	人结直肠癌癌组织与癌旁组织	激光显微切割技术	timsTOF Pro	/	/	536（15个细胞的体积）	/	[29]	癌症研究	[29]
ProteomEx	不需要	非标记	小鼠脑组织	激光显微切割技术	timsTOF Pro	/	/	928（160 μm横向分辨率）	/	[32]	图谱研究	[32]
DISCO-MS	不需要	非标记	小鼠脑、心脏和肺组织	激光显微切割技术	timsTOF Pro	/	/	1 400（每个ROI）	/	[34]	癌症研究	[34]
SCPro	不需要	非标记	HEK 293T	激光显微切割技术	timsTOF Pro	/	/	800（2.4个细胞的体积）	/	[35]	疾病精准分析	[35]

单细胞蛋白质组学数据分析软件Software for single-cell proteomics data analysis	构建方式Construction method	输出方式 Output method	适配的数据采集模式 Compatible data acquisition modes	测试数据集 Test datasets	特点 Features	参考文献 References
DO-MS	R	HTML报告	DDA、DIA	SCoPE、SCoPE2	交互式可视化分析	[19-20]
SCPCompanion	C#	XML文件	DDA	SCoPE、N2	推荐仪器和数据分析参数	[4,9,19]
SCeptre	Python	YAML文件	DDA	AML细胞群	数据归一化处理	[67]
DART-ID	Python和R	HTML报告	DDA	SCoPE、SCoPE2	对齐肽段保留时间	[19-20]
IceR	R	TSV文件	DDA（PIP）、DIA	DeMix-Q、IonStar、HRM-DIA等	使用离子电流信息进行混合肽段鉴定	[68]
DeepSCP	Python（深度学习）	CSV文件	DDA（TDC）	SCoPE2、N2	首次应用深度学习技术进行DDA分析	[4,20,69]

单细胞蛋白质组学数据分析软件Software for single-cell proteomics data analysis	构建方式Construction method	输出方式 Output method	适配的数据采集模式 Compatible data acquisition modes	测试数据集 Test datasets	特点 Features	参考文献 References
DO-MS	R	HTML报告	DDA、DIA	SCoPE、SCoPE2	交互式可视化分析	[19-20]
SCPCompanion	C#	XML文件	DDA	SCoPE、N2	推荐仪器和数据分析参数	[4,9,19]
SCeptre	Python	YAML文件	DDA	AML细胞群	数据归一化处理	[67]
DART-ID	Python和R	HTML报告	DDA	SCoPE、SCoPE2	对齐肽段保留时间	[19-20]
IceR	R	TSV文件	DDA（PIP）、DIA	DeMix-Q、IonStar、HRM-DIA等	使用离子电流信息进行混合肽段鉴定	[68]
DeepSCP	Python（深度学习）	CSV文件	DDA（TDC）	SCoPE2、N2	首次应用深度学习技术进行DDA分析	[4,20,69]